基于CEEMD和GWO的超短期风速预测  

Ultra-short-term forecasting of wind speed based on CEEMD and GWO

作　　者：王静 李维德[1] WANG Jing;LI Weide(School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, Chin)

机构地区：[1]兰州大学数学与统计学院,甘肃兰州730000

出　　处：《电力系统保护与控制》2018年第9期69-74,共6页Power System Protection and Control

基　　金：国家自然科学基金资助(41571016)~~

摘　　要：风电场风速预测对电力系统的合理调度、安全运行等方面有重大的影响。针对风速时间序列的非线性特征造成其预测精度不佳的问题,采用基于互补型集成经验模态分解和灰狼优化算法优化支持向量回归机的超短期风速组合预测模型来解决。首先利用该模型对非平稳的风速时间序列进行CEEMD分解,分解为一系列的相对平稳分量。然后对各个分量利用灰狼算法优化SVR进行预测。最后,将每一个分量的预测结果集成输出作为最终的风速预测结果。结果表明,该预测模型比其他智能算法基准模型预测精度高,且在风速预测中具有优越性。Forecasting of wind speed has a big influence on the rational dispatch and safety operation of electric power system. Aiming at the problem that the nonlinear characteristics of wind speed time series cause its poor prediction accuracy, a combined model based on complementary ensemble empirical mode decomposition and a support vector regression machine optimized by Gray Wolf Optimization （GWO） algorithm is used to predict ultra-short-term wind speed. First, the non-stationary wind speed time series is decomposed into a series of relatively stationary components by CEEMD. Then, each component is predicted by SVR optimized by GWO. Finally, the prediction values of each sequence are superimposed as the final prediction of wind speed. The results show that the prediction model is more accurate compared with other intelligent algorithm benchmark models and has superiority in wind speed prediction.

关 键 词：本征模态函数 互补型集成经验模态分解 支持向量回归机 灰狼优化算法 超短期风速预测 

分 类 号：TM614[电气工程—电力系统及自动化]